Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

测井资料在优选压裂层位中的应用

压裂是低孔、低渗油层改造的主要增产措施。根据常规测井资料、岩石物理实验分析数据及试油成果。研究储层四性关系，确定区块的低孔、低渗储层评价标准。进一步对油层进行分类，有针对性地为油层压裂提供施工层位和准确的储层参数。通过商543区块实例，阐述了选择压裂层位的方法。     

接轨站设置位置和布置图型的影响因素及其分类

通过对接轨站设置位置和布置图型主要影响因素的分析。按照能够充分反映接轨站作业特点的交接方式。对接轨站所采用的布置图型进行了归纳和分类。     

Biotransformation of l‐menthol by twelve isolates of soil‐borne plant pathogenic fungi (Rhizoctonia solani) and classification of fungi

The microbial transformation of l‐menthol ( 1 ) was investigated by using 12 isolates of soil‐borne plant pathogenic fungi, Rhizoctonia solani (AG‐1‐IA Rs24, Joichi‐2, RRG97‐1; AG‐1‐IB TR22, R147, 110.4; AG‐1‐IC F‐1, F‐4, P‐1; AG‐1‐ID RCP‐1, RCP‐3, and RCP‐7) as a biocatalyst. Rhizoctonia solani F‐1, F‐4 and P‐1 showed 89.7–99.9% yields of converted product from 1 , RCP‐1, RCP‐3, and RCP‐7 26.0–26.9% and the other isolates 0.1–12.0%. In the cases of F‐1, F‐4 and P‐1, substrate 1 was converted to (?)‐(1S,3R,4S,6S)‐6‐hydroxymenthol ( 2 ), (?)‐(1S,3R,4S)‐1‐hydroxymenthol ( 3 ) and (+)‐(1S,3R,4R,6S)‐6,8‐dihydroxymenthol ( 4 ), which was a new compound. Substrate 1 was converted to 2 and/or 3 by RRG97‐1, 110.4, RCP‐1, RCP‐3 and RCP‐7. The structures of the metabolic products were elucidated on the basis of their spectral data. In addition, metabolic pathways of the biotransformation of 1 by Rhizoctonia solani are discussed. Finally, from the main component analysis and the differences in the yields of converted product from 1 , the 12 isolates of Rhizoctonia solani were divided into three groups based on an analysis of the metabolites. Copyright © 2003 Society of Chemical Industry     

低温脂肪酶产生菌的筛选、鉴定及其部分酶学性质

从南极乔治王岛冻土来源的76株低温细菌中筛选到13株低温脂肪酶产生菌，对其中的BTsl0022菌株进行鉴定。通过生理生化特征、16s rDNA基因序列的同源性和系统发育分析发现，菌株RTsl0022属于假单胞菌属(Pseudomonas)，但与已定名的假单胞菌有一定的差异，与未定名的Pseudomonas sp．PsB的亲缘关系最接近，故将其暂定名为Pseudomonas sp．BTsl0022。对该菌脂肪酶的酶学性质初步研究表明，酶的最适作用温度为24℃，对热敏感，60℃处理30min仅残留25％酶活性，酶的适宜作用pH范围在7．0～9．0，最适pH为8．0。     

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.     

Precision equipment for symmetrical bending— Part 1. The loading system

The design and performance of precision bending equipment for testing straight beams with constant rectangular section under a pure bending moment is described. Part 1 deals with the loading arrangements and Part 2 with the rig for measuring beam deflection. The methods for recording deflection of strain gauges applied on the beam have been described elsewhere.^1–4
The reactions to the deadweight gravity loads are taken via ball bearings. The total bending moment inaccuracy, i.e. the sum of estimated systematic errors and random errors, the latter expressed as the 2Ga confidence interval about the mean (2G_cim), is about ± 25 x. 10^-4%, for 23 tests with a steel beam with 15 mm × 75 mm cross sectional dimensions, loaded to 1-1.5 mm/m strain.
The measuring rig stands on the beam via hardened and polished spherical segment feet and uses a capacitive transducer system with less than 5nm resolution. Various application tasks are described. The total inaccuracy of the measured deflection, i.e. apart from bending moment inaccuracy, is about ± 35 times 10^4% for about 10 reproduced, not repeated, tests with the same steel beam. The deflection for such a beam is of the order of 2 to 4 mm.     

基于DSP+CPLD的高精度信号发生器

介绍了基于直接数字式频率合成(DDS)原理的全数字信号发生器(DSP),利用DSP芯片快速、高精度的运算优势以及CPLD芯片灵活的编程逻辑、大容量存储功能的特点,采用通用可编程芯片以及数字波形合成技术,形成高稳定、高精度、高动态的数字合成信号.该信号发生器可产生0～25 kHz的正弦波、三角波和方波,输出电压峰峰值为0～5 V,频率步进1 Hz,幅度步进0.001 V.     

基于聚类算法实现信号盲分类

本文解决了信号处理、工业控制等领域存在的非平稳信号盲分类问题。在聚类中广泛应用的K-Means算法及其它基于中心的聚类算法有两个共同的缺陷-需要预先确定类数目且随机初始化中心引起性能不稳定。本文提出的算法较好地解决了这两个问题，提高了算法稳定性，实现了非平稳信号盲分类。提取非平稳信号的小波系数作为聚类的样本空间，分析聚类结果的统计偏差以估计类的数目，采用调和均值准则进行分类。最后给出的仿真结果表明本文提出的方法较传统的K-Means算法明显降低分类错误率。     

裂缝性油气藏采收率：100个裂缝性油气田实例的经验总结

通过对世界上100个裂缝性油气藏的综合评价，研究储集层及流体本身的性质(包括孔隙度、渗透率、黏度、可动油比例、含水饱和度、润湿性及裂缝分布特征等)和驱动机制及油藏管理战略(优化日产量和采用不同类型的提高采收率技术)对其最终采收率的影响。将裂缝性油气藏分为4类：I类的基质几乎没有孔隙度和渗透率，裂缝是储存空间和流体流动的通道；Ⅱ类的基质有较低的孔隙度和渗透率，基质提供储存空间，裂缝提供流动通道；Ⅲ类(微孔隙)的基质具有高孔隙度和低渗透率，基质提供储存空间，裂缝提供流动通道；Ⅳ类(大孔隙)的基质具有高的孔隙度和渗透率，基质提供储存空间和流动通道，裂缝仅增加渗透率。对26个Ⅱ类油气藏和20个Ⅲ类油气藏的开采历史的研究表明：Ⅱ类油气藏的采收率受水驱强度和最优日产量控制，日产量过高会很容易破坏Ⅱ类油气藏，一些Ⅱ类油气藏如果管理得当，采收率可以很高，不需要二次或三次采油；Ⅲ类油气藏的采收率主要受岩石和流体本身性质的影响，特别是基质渗透率、流体重度、润湿性以及裂缝强度等，不进行二次或三次采油不可能完全开采，往往需要采用一些提高采收率的专门技术。以往将Ⅱ类和Ⅲ类裂缝性油气藏归为一类，认识它们的区别将有助于选择更好的开发策略。